OpenRaiser/Intern-Atlas huggingface dataset:
Modified Distribution Alignment for Domain Adaptation with Pre-trained Inception ResNet
Venue: arXiv cs.CV 2019
Year: 2019

Deep neural networks have been widely used in computer vision. There are several well trained deep neural networks for the ImageNet classification challenge, which has played a significant role in image recognition. However, little work has explored pre-trained neural networks for image recognition in domain adaption. In this paper, we are the first to extract better-represented features from a pre-trained Inception ResNet model for domain adaptation. We then present a modified distribution alignment method for classification using the extracted features. We test our model using three benchmark datasets (Office+Caltech-10, Office-31, and Office-Home). Extensive experiments demonstrate significant improvements (4.8%, 5.5%, and 10%) in classification accuracy over the state-of-the-art.
Mining Precision Interfaces From Query Logs
Venue: arXiv cs.DB 2019
Year: 2019

Interactive tools make data analysis more efficient and more accessible to end-users by hiding the underlying query complexity and exposing interactive widgets for the parts of the query that matter to the analysis. However, creating custom tailored (i.e., precise) interfaces is very costly, and automated approaches are desirable. We propose a syntactic approach that uses queries from an analysis to generate a tailored interface. We model interface widgets as functions I(q) -> q' that modify the current analysis query $q$, and interfaces as the set of queries that its widgets can express. Our system, Precision Interfaces, analyzes structural changes between input queries from an analysis, and generates an output interface with widgets to express those changes. Our experiments on the Sloan Digital Sky Survey query log suggest that Precision Interfaces can generate useful interfaces for simple unanticipated tasks, and our optimizations can generate interfaces from logs of up to 10,000 queries in <10s.
Measurement-Induced Boolean Dynamics and Controllability for Quantum Networks
Venue: arXiv cs.SY 2019
Year: 2019

In this paper, we study dynamical quantum networks which evolve according to Schrödinger equations but subject to sequential local or global quantum measurements. A network of qubits forms a composite quantum system whose state undergoes unitary evolution in between periodic measurements, leading to hybrid quantum dynamics with random jumps at discrete time instances along a continuous orbit. The measurements either act on the entire network of qubits, or only a subset of qubits. First of all, we reveal that this type of hybrid quantum dynamics induces probabilistic Boolean recursions representing the measurement outcomes. With global measurements, it is shown that such resulting Boolean recursions define Markov chains whose state-transitions are fully determined by the network Hamiltonian and the measurement observables. Particularly, we establish an explicit and algebraic representation of the underlying recursive random mapping driving such induced Markov chains. Next, with local measurements, the resulting probabilistic Boolean dynamics is shown to be no longer Markovian. The state transition probability at any given time becomes dependent on the entire history of the sample path, for which we establish a recursive way of computing such non-Markovian probability transitions. Finally, we adopt the classical bilinear control model for the continuous Schrödinger evolution, and show how the measurements affect the controllability of the quantum networks.
Efficient GAN-based method for cyber-intrusion detection
Venue: arXiv cs.LG 2019
Year: 2019

Ubiquitous anomalies endanger the security of our system constantly. They may bring irreversible damages to the system and cause leakage of privacy. Thus, it is of vital importance to promptly detect these anomalies. Traditional supervised methods such as Decision Trees and Support Vector Machine (SVM) are used to classify normality and abnormality. However, in some case the abnormal status are largely rarer than normal status, which leads to decision bias of these methods. Generative adversarial network (GAN) has been proposed to handle the case. With its strong generative ability, it only needs to learn the distribution of normal status, and identify the abnormal status through the gap between it and the learned distribution. Nevertheless, existing GAN-based models are not suitable to process data with discrete values, leading to immense degradation of detection performance. To cope with the discrete features, in this paper, we propose an efficient GAN-based model with specifically-designed loss function. Experiment results show that our model outperforms state-of-the-art models on discrete dataset and remarkably reduce the overhead.
Computational Limitations of Affine Automata
Venue: arXiv cs.FL 2019
Year: 2019

We present two new results on the computational limitations of affine automata. First, we show that the computation of bounded-error rational-values affine automata is simulated in logarithmic space. Second, we give an impossibility result for algebraic-valued affine automata. As a result, we identify some unary languages (in logarithmic space) that are not recognized by algebraic-valued affine automata with cutpoints.
Useful Statistical Methods for Human Factors Research in Software Engineering: A Discussion on Validation with Quantitative Data
Venue: arXiv cs.SE 2019
Year: 2019

In this paper we describe the usefulness of statistical validation techniques for human factors survey research. We need to investigate a diversity of validity aspects when creating metrics in human factors research, and we argue that the statistical tests used in other fields to get support for reliability and construct validity in surveys, should also be applied to human factors research in software engineering more often. We also show briefly how such methods can be applied (Test-Retest, Cronbach's α, and Exploratory Factor Analysis).
Agility Measurements Mismatch: A Validation Study on Three Agile Team Assessments in Software Engineering
Venue: arXiv cs.SE 2019
Year: 2019

Many tools have been created for measuring the agility of software teams, thus creating a saturation in the field. Three agile measurement tools were selected in order to validate whether they yield sim-ilar results. The surveys of the tools were given to teams in Company A (N = 30). The questions were grouped into agile practices which were checked for correlation in order to establish convergent validity. In addition, we checked whether the questions identified to be the same among the tools would be given the same replies by the respondents. We could not establish convergent validity since the correlations of the data gathered were very few and low. In addition, the questions which were identified to have the same meaning among the tools did not have the same answers from the respondents. We conclude that the area of measuring agility is still immature and more work needs to be done. Not all tools are applicable to every team but they should be selected on the basis of how a team has transitioned to agile.
Trying to Increase the Mature Use of Agile Practices by Group Development Psychology Training - An Experiment
Venue: arXiv cs.SE 2019
Year: 2019

There has been some evidence that agility is connected to the group maturity of software development teams. This study aims at conducting group development psychology training with student teams, participating in a project course at university, and compare their group effectiveness score to their agility usage over time in a longitudinal design. Seven XP student teams were measured twice (43+40), which means 83 data points divided into two groups (an experimental group and one control group). The results showed that the agility measurement was not possible to increase by giving a 1.5-hour of group psychology lecture and discussion over a two-month period. The non-significant result was probably due to the fact that 1.5 hours of training were not enough to change the work methods of these student teams, or, a causal relationship does not exist between the two concepts. A third option could be that the experiential setting of real teams, even at a university, has many more variables not taken into account in this experiment that affect the two concepts. We therefore have no conclusions to draw based on the expected effects. However, we believe these concepts have to be connected since agile software development is based on teamwork to a large extent, but there are probably many more confounding or mediating factors.
The investigation of social networks based on multi-component random graphs
Venue: arXiv cs.SI 2019
Year: 2019

The methods of non-homogeneous random graphs calibration are developed for social networks simulation. The graphs are calibrated by the degree distributions of the vertices and the edges. The mathematical foundation of the methods is formed by the theory of random graphs with the nonlinear preferential attachment rule and the theory of Erdos-Renyi random graphs. In fact, well-calibrated network graph models and computer experiments with these models would help developers (owners) of the networks to predict their development correctly and to choose effective strategies for controlling network projects.
Mixed Random Sampling of Frames method for counting number of motifs
Venue: arXiv cs.SI 2019
Year: 2019

The problem of calculating the frequencies of network motifs on three and four nodes in large networks is considered. Telecommunications networks, cell molecular networks are investigated. The sizes of the investigated networks are hundreds of thousands of nodes and connections. These networks are represented in the form of directed and undirected simple graphs. Exact calculating requires huge computational resources for such large graphs. A method for calculating the frequencies of network motifs using the Monte Carlo method with control of the accuracy of calculations is proposed. The proposed effective method minimizes the value of the coefficient of variation.
Sharing a pizza: bisecting masses with two cuts
Venue: arXiv cs.CG 2019
Year: 2019

Assume you have a pizza consisting of four ingredients (e.g., bread, tomatoes, cheese and olives) that you want to share with your friend. You want to do this fairly, meaning that you and your friend should get the same amount of each ingredient. How many times do you need to cut the pizza so that this is possible? We will show that two straight cuts always suffice. More formally, we will show the following extension of the well-known Ham-sandwich theorem: Given four mass distributions in the plane, they can be simultaneously bisected with two lines. That is, there exist two oriented lines with the following property: let $R^+_1$ be the region of the plane that lies to the positive side of both lines and let $R^+_2$ be the region of the plane that lies to the negative side of both lines. Then $R^+=R^+_1\cup R^+_2$ contains exactly half of each mass distribution.
x3ogre: Connecting X3D to a state of the art rendering engine
Venue: arXiv cs.GR 2019
Year: 2019

We connect X3D to the state of the art OGRE renderer using our prototypical x3ogre implementation. At this we perform a comparison of both on a conceptual level, highlighting similarities and differences. Our implementation allows swapping X3D concepts for OGRE concepts and vice versa. We take advantage of this to analyse current shortcomings in X3D and propose X3D extensions to overcome those.
Noise-Level Estimation from Single Color Image Using Correlations Between Textures in RGB Channels
Venue: arXiv cs.CV 2019
Year: 2019

We propose a simple method for estimating noise level from a single color image. In most image-denoising algorithms, an accurate noise-level estimate results in good denoising performance; however, it is difficult to estimate noise level from a single image because it is an ill-posed problem. We tackle this problem by using prior knowledge that textures are highly correlated between RGB channels and noise is uncorrelated to other signals. We also extended our method for RAW images because they are available in almost all digital cameras and often used in practical situations. Experiments show the high noise-estimation performance of our method in synthetic noisy images. We also applied our method to natural images including RAW images and achieved better noise-estimation performance than conventional methods.
Segmentation of the Prostatic Gland and the Intraprostatic Lesions on Multiparametic MRI Using Mask-RCNN
Venue: arXiv cs.CV 2019
Year: 2019

Prostate cancer (PCa) is the most common cancer in men in the United States. Multiparametic magnetic resonance imaging (mp-MRI) has been explored by many researchers to targeted prostate biopsies and radiation therapy. However, assessment on mp-MRI can be subjective, development of computer-aided diagnosis systems to automatically delineate the prostate gland and the intraprostratic lesions (ILs) becomes important to facilitate with radiologists in clinical practice. In this paper, we first study the implementation of the Mask-RCNN model to segment the prostate and ILs. We trained and evaluated models on 120 patients from two different cohorts of patients. We also used 2D U-Net and 3D U-Net as benchmarks to segment the prostate and compared the model's performance. The contour variability of ILs using the algorithm was also benchmarked against the interobserver variability between two different radiation oncologists on 19 patients. Our results indicate that the Mask-RCNN model is able to reach state-of-art performance in the prostate segmentation and outperforms several competitive baselines in ILs segmentation.
MoA Interpretation of the Iterative Conjugate Gradient Method with Psi Reduction - A Tutorial to teach the Mathematically literate in Linear and Tensor Algebra: Part I
Venue: arXiv cs.OH 2019
Year: 2019

It is often difficult to learn new mathematics semantically and syntactically, even when there are similarities in the words and meaning when discussed aloud. The goal of this document is to facilitate learning through explanations and definitions relating our common mathematical knowledge and highlighting what is new. It is meant to be a working document that will evolve based on feedback from target audiences, those mathematically literate in linear and tensor algebra, those that want to learn MoA, Psi Calculus, and its uses, those that want and need the ability to prove a design, either in hardware or software through the ONF, Operational Normal Form, and those wanting to exploit all resources optimally, especially when Tensor Algebra, i.e. algorithms foundational to their application,are needed: Knowledge Representation, Machine Learning, Signal Processing, AI, HPC, etc.
End-to-End Video Captioning
Venue: arXiv cs.CV 2019
Year: 2019

Building correspondences across different modalities, such as video and language, has recently become critical in many visual recognition applications, such as video captioning. Inspired by machine translation, recent models tackle this task using an encoder-decoder strategy. The (video) encoder is traditionally a Convolutional Neural Network (CNN), while the decoding (for language generation) is done using a Recurrent Neural Network (RNN). Current state-of-the-art methods, however, train encoder and decoder separately. CNNs are pretrained on object and/or action recognition tasks and used to encode video-level features. The decoder is then optimised on such static features to generate the video's description. This disjoint setup is arguably sub-optimal for input (video) to output (description) mapping. In this work, we propose to optimise both encoder and decoder simultaneously in an end-to-end fashion. In a two-stage training setting, we first initialise our architecture using pre-trained encoders and decoders -- then, the entire network is trained end-to-end in a fine-tuning stage to learn the most relevant features for video caption generation. In our experiments, we use GoogLeNet and Inception-ResNet-v2 as encoders and an original Soft-Attention (SA-) LSTM as a decoder. Analogously to gains observed in other computer vision problems, we show that end-to-end training significantly improves over the traditional, disjoint training process. We evaluate our End-to-End (EtENet) Networks on the Microsoft Research Video Description (MSVD) and the MSR Video to Text (MSR-VTT) benchmark datasets, showing how EtENet achieves state-of-the-art performance across the board.
Tiered Latent Representations and Latent Spaces for Molecular Graphs
Venue: arXiv cs.LG 2019
Year: 2019

Molecular graphs generally contain subgraphs (known as groups) that are identifiable and significant in composition, functionality, geometry, etc. Flat latent representations (node embeddings or graph embeddings) fail to represent, and support the use of, groups. Fully hierarchical latent representations, on the other hand, are difficult to learn and, even if learned, may be too complex to use or interpret. We propose tiered latent representations and latent spaces for molecular graphs as a simple way to explicitly represent and utilize groups, which consist of the atom (node) tier, the group tier and the molecule (graph) tier. Specifically, we propose an architecture for learning tiered latent representations and latent spaces using graph autoencoders, graph neural networks, differentiable group pooling and the membership matrix. We discuss its various components, major challenges and related work, for both a deterministic and a probabilistic model. We also briefly discuss the usage and exploration of tiered latent spaces. The tiered approach is applicable to other types of structured graphs similar in nature to molecular graphs.
Deep Multi-class Adversarial Specularity Removal
Venue: arXiv cs.CV 2019
Year: 2019

We propose a novel learning approach, in the form of a fully-convolutional neural network (CNN), which automatically and consistently removes specular highlights from a single image by generating its diffuse component. To train the generative network, we define an adversarial loss on a discriminative network as in the GAN framework and combined it with a content loss. In contrast to existing GAN approaches, we implemented the discriminator to be a multi-class classifier instead of a binary one, to find more constraining features. This helps the network pinpoint the diffuse manifold by providing two more gradient terms. We also rendered a synthetic dataset designed to help the network generalize well. We show that our model performs well across various synthetic and real images and outperforms the state-of-the-art in consistency.
An End-to-End Conversational Style Matching Agent
Venue: arXiv cs.HC 2019
Year: 2019

We present an end-to-end voice-based conversational agent that is able to engage in naturalistic multi-turn dialogue and align with the interlocutor's conversational style. The system uses a series of deep neural network components for speech recognition, dialogue generation, prosodic analysis and speech synthesis to generate language and prosodic expression with qualities that match those of the user. We conducted a user study (N=30) in which participants talked with the agent for 15 to 20 minutes, resulting in over 8 hours of natural interaction data. Users with high consideration conversational styles reported the agent to be more trustworthy when it matched their conversational style. Whereas, users with high involvement conversational styles were indifferent. Finally, we provide design guidelines for multi-turn dialogue interactions using conversational style adaptation.
Intent-Aware Probabilistic Trajectory Estimation for Collision Prediction with Uncertainty Quantification
Venue: arXiv cs.RO 2019
Year: 2019

Collision prediction in a dynamic and unknown environment relies on knowledge of how the environment is changing. Many collision prediction methods rely on deterministic knowledge of how obstacles are moving in the environment. However, complete deterministic knowledge of the obstacles' motion is often unavailable. This work proposes a Gaussian process based prediction method that replaces the assumption of deterministic knowledge of each obstacle's future behavior with probabilistic knowledge, to allow a larger class of obstacles to be considered. The method solely relies on position and velocity measurements to predict collisions with dynamic obstacles. We show that the uncertainty region for obstacle positions can be expressed in terms of a combination of polynomials generated with Gaussian process regression. To control the growth of uncertainty over arbitrary time horizons, a probabilistic obstacle intention is assumed as a distribution over obstacle positions and velocities, which can be naturally included in the Gaussian process framework. Our approach is demonstrated in two case studies in which (i), an obstacle overtakes the agent and (ii), an obstacle crosses the agent's path perpendicularly. In these simulations we show that the collision can be predicted despite having limited knowledge of the obstacle's behavior.
On Error Rate Analysis for URLLC over Multiple Fading Channels
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we study ultra-reliable and low-latency communication (URLLC) under fading using multiple frequency or time bins. We investigate an approach to find an upper-bound on the packet error rate when a finite-length code is used. From simulation results, we find that the bound is reasonably tight for wide ranges of signal-to-noise ratio (SNR) and the number of multiple bins. Thus, the derived bound can be used to determine key parameters to guarantee the performance of URLLC in terms of the packet error rate.
An Evolutionary Framework for Automatic and Guided Discovery of Algorithms
Venue: arXiv cs.NE 2019
Year: 2019

This paper presents Automatic Algorithm Discoverer (AAD), an evolutionary framework for synthesizing programs of high complexity. To guide evolution, prior evolutionary algorithms have depended on fitness (objective) functions, which are challenging to design. To make evolutionary progress, instead, AAD employs Problem Guided Evolution (PGE), which requires introduction of a group of problems together. With PGE, solutions discovered for simpler problems are used to solve more complex problems in the same group. PGE also enables several new evolutionary strategies, and naturally yields to High-Performance Computing (HPC) techniques. We find that PGE and related evolutionary strategies enable AAD to discover algorithms of similar or higher complexity relative to the state-of-the-art. Specifically, AAD produces Python code for 29 array/vector problems ranging from min, max, reverse, to more challenging problems like sorting and matrix-vector multiplication. Additionally, we find that AAD shows adaptability to constrained environments/inputs and demonstrates outside-of-the-box problem solving abilities.